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Carcinogenesis vol.35 no.1 pp.201–207, 2014Advance Access publication August 12, 2013Assessing compound carcinogenicity in vitro using connectivity mappingFlorian Caiment, Maria Tsamou, Danyel Jennen and
a classifying in vitro model for the human situation in vivo remains
a challenge, due to the high heterogeneity of human cancers and the
difficulty to build accurate cancer biomarkers.
Department of Toxicogenomics, Maastricht University, 6200 Maastricht, The
Recently, a systematic approach referred to as ‘connectivity map-
ping' (Cmap) was proposed to establish putative connections between a
*To whom correspondence should be addressed. Department of
signature profile characteristic of any biological states and a core data-
Toxicogenomics, Maastricht University, Universiteitsingel 50, 6200
base of gene e). For instance, Cmap allows the
Maastricht, The Netherlands. Tel: +31 43 3881089; Fax: +31 43 3884146;
prediction of the molecular response to a new chemical entity by linking
its observed effect on the gene expression profile with similar (in case
One of the main challenges of toxicology is the accurate predic-
of positive connection) or antagonist (negative connection) compounds.
tion of compound carcinogenicity. The default test model for
Applied to a disease state signature, Cmap may also predict whether
assessing chemical carcinogenicity, the 2 year rodent cancer
small bioactive molecules are capable of causing or preventing this par-
bioassay, is currently criticized because of its limited specific-
ticular disease. The authors demonstrated the viability of the method
ity. With increased societal attention and new legislation against
with respect to several biological processes, including complex disease
animal testing, toxicologists urgently need an alternative to the
states such as Alzheimer disease or diet-induced obesity, be it with vari-
current rodent bioassays for chemical cancer risk assessment.
able success. One of the main advantages of this method, based on the
Toxicogenomics approaches propose to use global high-through-
non-parametric Kolmogorov–Smirnov test, is the possibility to use any
put technologies (transcriptomics, proteomics and metabolomics)
platform technology combination. Cmap has consequently been used
to study the toxic effect of compounds on a biological system.
for a variety of purposes including finding disease alternative treat-
Here, we demonstrate the improvement of transcriptomics assay
ments, elucidation of mode of action of drugs, drug repurposing and
consisting of primary human hepatocytes to predict the putative
systems biology approaches (revie
liver carcinogenicity of several compounds by applying the con-
In this study, we propose to exploit hepatocellular carcinoma (HCC)
nectivity map methodology. Our analyses underline that connec-
as a model to investigate the power of the Cmap method for predicting
tivity mapping is useful for predicting compound carcinogenicity
and classifying the putative liver carcinogenicity of a compound in by connecting in vivo expression profiles from human cancer tis-vitro, the liver being the main target organ in the 2 year rodent cancer
sue samples with in vitro toxicogenomics data sets. Furthermore,
assay. HCC is the most common form of liver cancer in humans, and
the importance of time and dose effect on carcinogenicity predic-
the third cause of cancer mortality. Liver carcinogenesis is a complex
tion is demonstrated, showing best prediction for low dose and
multifactor mechanism with several possible etiologies among which
24 h exposure of potential carcinogens.
several viruses, such as Hepatitis B virus and Hepatitis C virus, are
known to play a role, along with environmental and chemical expo-
sures such as ethanol or aflin vitro
liver systems have been well studied and multiple relevant data sets
are publicly available, thus allowing the collection of sufficient data to
perform a genome-wide Cmap analysis.
For over 40 years, carcinogenic properties of both natural and syn-
thetic compounds have been estimated using the 2 year rodent bioas-
Materials and methods
say. This laborious, time consuming and expensive test kills a large
number of animals. However, only 60% of its predictions are relevant
According to the Cmap method design (
controversy on the model's
between liver carcinogenicity and any given compound first necessitates the
). Moreover, chemical manufacturing companies face an
building of a signature query describing the disease (in our case HCC) and of a
increasingly restrictive legislation against animal testing (,). Taken
widely ranging in vitro gene profiling ‘reference' data set ().
together, toxicology urgently needs alternative non-animal testing
HCC signature geneset
methods to improve compound carcinogenicity predictions. Several
To establish our HCC signature geneset, we took advantage of publicly avail-
alternative methods already exist, such as the quantitative alterna-
able microarray e
tive structure relationship method, the quantum mechanics/molecular
) and in several steps selected the final list of genes. First, we used
mechanics or the threshold of toxicological concern, but all feature
the Expression Project for Oncology (expO) study (E-GEOD-2109 on array-
major flaws (for reviews, see refs
Express), which performed gene expression analyses on a clinically annotated
Global gene expression profiling (i.e. transcriptomics), applied to in
set of a large panel of different tumor samples using a total of 2158 arrays. Among the 42 samples labeled as ‘liver sample' in expO (each belonging to
vitro systems representative of target organs in vivo, is also considered
a unique donor), we selected the 10 arrays strictly identified as HCC. These
a putative alternative to animal testing. Some promising results, nota-
10 arrays, all hybridized on Affymetrix GeneChip Human Genome U133 Plus
bly using the cancer liver cell line HepG2, have been obtained show-
2.0 Arrays, were subjected to a quality control using the ArrayAnalysis pipe-
ing that toxicogenomic-based approaches are capable of significantly
discriminating carcinogenic subclasses (,The overall accuracy of
Averaging method (for Multichip Average) and reannotated using the Custom
these toxicogenomics approaches have been estimated to be around
CDF version 14 with Entrez Gene identifiers.
80% for predicting in vivo). A recent study,
Second, healthy liver sample gene expression profiles generated by the
based on genesets selected after stratification of chemicals combined
same Affymetrix genechip were used as control. Three studies corresponding
with results from the classical Ames mutagenicity assay, reached a
to these characteristics were found: E-GEOD-11045 (vestigating three normal liv) analyzing two normal liver sam-
compound prediction accuracy of 89%, with a sensitivity of 91% and
ples and E-GEOD-15238 (ving studied three liver samples from donors
a specificity of 87% (). However, demonstrating the relevance of
of different age (1.5, 42 and 81 years). The prenatal liver sample from the E-GEOD-15238 study was not used. All normal liver arrays were subjected to quality control and subsequently normalized and annotated similar to the
Abbreviations: CHL, carcinogenic in human liver; Cmap, connectivity map-
cancer samples.
ping; CNL, non-liver carcinogens; CRL, carcinogenic in rodent liver; expO,
Reference data set
Expression Project for Oncology; HCC, hepatocellular carcinoma; IARC, International Agency for Research on Cancer; NC, non-carcinogens; PHH,
To build the reference data set, all human liver in vitro microarray data files
primary human hepatocytes; UC, unknown carcinogenicity.
from the Open TG_Gates database ) were
The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]Fig. 1. Analysis flowchart. Global flowchart of the sscMAP analysis, with the in vivo HCC signature building and the reference data set composition.
downloaded. This in vitro toxicogenomics data set presents a total of 158 com-
connectivity score with each reference file from the TG_Gates data set,
pounds applied in duplicate to primary human hepatocytes (PHH) at up to three
by summing the product of each individual gene in the signature with its
doses (low, middle and high doses) and 3 time points (2, 8 and 24 h) per com-
signed ranked in the reference file. Thus, a gene in the query described
pound. The high dose was determined for each compound as the maximally
as upregulated in HCC (+1) will increase the connectivity score if this
tolerated dose. Middle and low doses were defined by taking a concentration of
gene is upregulated in the reference (positive rank) or decrease the score
1/5 and 1/25 of the highest, respectively. Each treated compound condition is
if downregulated in the reference (negative rank). A P value was gener-
associated with a control, using PHH from the same donor. After curation, 10
ated for each connectivity score using random permutation of the geneset.
compounds with inconsistent array data (missing 24 h time point condition or
The analysis was carried out using the default parameter recommended
duplicates; available at Carcinogenesis Online) were
by sscMAP: a number of random permutations of 10 000 (leading to a
removed, as well as a further nine compounds for which effects on other hepatic
minimum P value of 10−4), a random seed of 0 and an expected number of
endpoints of toxicity than cancer were av,
false connections to tolerate of 1. The threshold of significance was set at
available at Carcinogenesis Online). This curation thus resulted in a reference
1/N, with N the number of treatment instances in the reference database
data set of 139 different compounds, corresponding to 2320 microarrays, all
in the given analysis.
similarly generated by Affymetrix GeneChip Human Genome U133 Plus 2.0
In order to parse the results, a scoring system was used together with plot-
Arrays. Each array was processed in the same way as described above for the
ting algorithms. The connection between the query signature and a reference
arrays used for building the HCC signature geneset. Each treatment condition
instance were assessed by computing significance score (S score) for the CHL
was converted into an instance reference file (as defined by Lamb et al.),
and NC compounds: S score pCHL
i.e. a treatment associated to its control pair) according to the sscMAP soft-
With pCHL and pNC representing the percentage of instances significantly
w). Briefly, each gene was associated with a score correspond-
and positively connected for CHL and NC, respectively.
ing to its signed rank in the microarray global expression panel.
Ideally, we expect the entire CHL group compounds, our testing set, to be
positively connected and above the threshold of significance after correction
for multiple testing. All NC group compounds (non-liver carcinogens) are
Based on publicly available carcinogenicity data collected by different inter-
expected to be at least below this same threshold, or even negatively connected
national or national agencies specialized in cancer research [International
to the query. Our scoring system will give an S score of 100 for this perfect
Agency for Research on Cancer (IARC), Carcinogenic Potency Database,
condition, and this S score will decrease for each CHL or NC compound mis-
National Toxicology Program and Environmental Protection Agency],
classified. A score of 0 would indicate a random distribution of the various
together with additional research on literature and personnel knowledge, all
groups and a score of −100 would correspond to the extreme opposite scenario
the compounds used in the open TG-Gates project were classified according
(all NC compound are significantly and positively connected to the HCC sig-
to their known carcinogenicity (, available
nature, and all CHL are not).
at Carcinogenesis Online). Taking into account the two main end points for carcinogenicity of the compounds in view of the aims of this study, namely target test system (liver) and target species (human–rodents), a new classifica-
tion list for hepatocarcinogenicity was created. The list consists of five groups, where two groups contain compounds with liver carcinogenic activities clearly
HCC signature geneset composition
reported on humans [carcinogenic in human liver (CHL)] or only available on rodents [carcinogenic in rodent liver (CRL)]. The group comprising of non-
The HCC signature geneset was built by taking all the commonly
liver carcinogens present the known human and/or rodent carcinogenic com-
up and downregulated genes in the 10 liver cancer samples from the
pounds in non-liver target organs (species being distinguished by the IARC
expO project compared with the normal liver samples, regardless of
classification). The non-carcinogens (NC) group consists of established non-
any arbitrary cutoff threshold. The HCC final signature contained
carcinogens in both species. Finally, the unknown carcinogenicity (UC) group
7520 upregulated and 1414 downregulated genes that were assigned
comprises compounds for which no clear information is available. The final
with a score of +1 for the upregulated genes and −1 for the downregu-
, available at Carcinogenesis Online).sscMAP analysis
We then set out to check whether the gene expressions com-
Connectivity between the geneset with all individual instances compos-
monly associated with HCC carcinogenesis in literature were actu-
ing our reference data set was computed using the sscMAP JAVA soft-
ally present in our selected HCC signature geneset. We established
w, our HCC geneset signature was used to compute a
a list of the 36 most commonly reported genes (
Compound carcinogenicity using connectivity mappingTable I. Reference compound classification
Chlorpheniramine maleate
Carbon tetrachloride
Interleukin 1 beta, human
Tumor Necrosis Factor α
Interleukin 6, human
Transforming Growth Factor β1
Iproniazid phosphate
Phenylanthranilic acid
Enalapril maleate
Methylene dianiline
Rosiglitazone maleate
Buthionine sulfoximine
Butylated hydroxyanisole
Fluoxetine hydrochloride
Naphthyl isothiocyanate
Hepatocyte growth factor
Open TG_Gates dataset contains 158 different compounds applied on PHH from which we selected 139. Each compound is classified based on its predicted carcinogenicity [CHL, CRL, CNL (non-liv, available at Carcinogenesis Online, for a complete data set annotation.
, available at Carcinogenesis Online) from literature reviews
sscMAP analysis
strictly restricted to human cases combined with the EHCO II
The sscMAP analysis of our HCC signature geneset against the TG_
database (gulated
Gates data set produce 829 connectivity scores, one for each indi-
(among which COL5A2, GPC3, MDK, TP53BP2, XPO1) and
vidual compound dose and time point. All the S scores are presented
12 downregulated (including HGFAC, IGFALS, LCAT, MT2A,
SLC22A1). Surprisingly, only 40% (15/36) of those genes were
Plotted all together, no particularly important pattern appears
found in common with our HCC signature geneset. Notably, GPC3
(wever, where the complete set fails to classify the
(glypican 3), a gene known to be overexpressed in most HCC and
carcinogenic compounds correctly, dividing the arrays according to
earlier proposed as a HCC biomarker(as not conserved in
experimental factors greatly improv
our signature. Although GPC3 was indeed highly expressed in
Hence, experiments using a low concentration of the compounds
five of the expO HCC cancer samples, in contrast, two samples
with an exposure duration of 24 h demonstrate the best classification,
presented a downregulation compared with the normal liver con-
trol pool, causing this particular gene to be excluded from our
signature geneset.Table II. sscMap S scoreCmap reference data set
The reference data set was built from the in vitro human microarray
data in the Open TG_Gates ) (),
all derived from the same cell model (PHH) and microarray platform
(Affymetrix hg-u133 plus 2.0). Data from 139 compounds were con-
verted to 829 individual instances. We found clear liver carcinogenic
reports in human for three compounds, i.e. ethanol, aflatoxin B1 and
S Score computed for our HCC signature geneset for each experimental factor condition on the TG_Gates reference geneset. A score of 0 would
azathioprine, which thus defined our positive control testing group
indicate a random distribution of the various groups, a score of 100
(CHL group). The full reference details of all compounds are available
correspond to the perfect classification (all CHL compound are significantly
in and vailable at Carcinogenesis
and positively connected to the HCC signature and all NC are not) and a
score of −100 would represent the opposite scenario.Fig. 2. sscMap plot results. sscMAP plot using our HCC signature geneset against the TG_Gates reference data set. Each dot represents a compound instance (unique dose and time condition compared with the control). The color code indicates the compound classification, as indicated in the legend. For the CRL group, the IARC classification information is added (i1 for group 1, i2 for group 2 and i3 for group 3 or no IARC classification). The green horizontal line present the threshold corrected for multiple testing. The S score is indicated on the bottom left of each graph (A) Complete analysis, displaying all doses and time points available in the reference data set. (B) Same plot displaying only arrays made at low concentration and 24 h compound exposure. (C) Same plot displaying only arrays made at high concentration and 8 h compound exposure. (D) Same plot displaying only arrays made at high concentration and 24 h compound exposure.
yielding a S score of 80 (o
concentration and exposure time a positive connection for 66.7% (10
CHL compounds (ethanol was not tested at low concentration in our
of 15) of the NC compounds is displayed. This tendency toward mis-
reference dataset) are correctly positively connected (at the low-
classification decreases with time but remains at the level of 40% (6
est P value) but also all NC compounds, except for cimetidine, are
out of 15) for the NC compounds after 24 h of exposure (
significantly negatively connected (thus a total of 85.7% compound
The same observation but with less amplitude is made for the medium
correctly classified). The rodent hepatocarcinogenic compounds
(CRL), for which no data on human carcinogenicity are available, are
represented here characterized by their available IARC (Agency for
Research on Cancer) classification. The highest connectivity score
In order to validate predictions of hepatocarcinogenic properties in
comes from azathioprine, a CHL compound, followed by omeprazole
vitro based on our in vivo HCC signature geneset, we used in vitro tox-
(UC) and by cyclosporine A that represents the only CRL compound
icogenomic data from two compounds, tested in-house also in PHH:
classified as IARC group 1 (a group of 111 agents reported to be
aflatoxin B1, a CHL compound also present in the TG_Gates data set,
carcinogenic to humans, independently of tissue specificity) in this
and Benzo[a]pyrene, an IARC group 1 compound known for its hepa-
setting. Several other CRL compounds with lower (or no) classifica-
tocarcinogenicity in mice. This validation data set has been generated
tion in IARC also yield a significantly high positive connection score,
similarly to the study protocol of the TG_Gates assays (three doses,
like methapyrilene, thioacetamide and ethinylestradiol, tending to
Affymetrix platform, PHH) but has been derived only after one expo-
prove a possible carcinogenic role in human liver. However, three CRL
sure period (24 h). Those data were added to our reference data set,
compounds (phenytoin, phenobarbital and gemfibrozil) with demon-
and a new sscMAP analysis was performed (). Interestingly,
strated carcinogenicity in rodent are negatively connected to the in
the resulting data set generated a significant positive connection at
vivo human signature and thus could be questioned regarding their
all experimental condition for aflatoxin B1, similarly as obtained for
carcinogenicity in humans. Globally, we observed that the classifica-
aflatoxin using the TG_Gates data set, thus confirming the high poten-
tion improves when the dose decreases at 24 h e).
tial of aflatoxin B1 for inducing liver carcinogenicity. However, the
The lowest S score (−26,6) is observed at high-dose concentra-
scores from our data set are always higher. Benzo[a]pyrene displays
tion and 8 h everal
a significant positive connection when using data from both low- and
NC compounds are positively connected with HCC. Hence, at this
high-incubation concentrations. Data obtained upon BaP exposure at
Compound carcinogenicity using connectivity mappingTable III. sscMap results for low concentration and 24 h only
Transforming Growth Factor β1
Tumor necrosis factor α
Carbon tetrachloride
Naphthyl isothiocyanate
Interleukin 1 β
Buthionine sulfoximine
Butylated hydroxyanisole
Rosiglitazone maleate
Methylene dianiline
Hepatocyte growth factor
Fluoxetine hydrochloride
Table III. Continued
Each row corresponds to a microarray using a given compound, at low concentration and at 24 h exposure. The connectivity score (setscore) and the corresponding P value are given. A significance mark of 1 indicates significance above the threshold corrected for multiple testing.
usually linked to HCC in literature and databases failed to establish
any significant connection with the CHL compounds (data not shown).
We believe this could be explained by the complexity of the carcino-
genic mechanisms that cannot be resumed within a small list of bio-
marker genes. This observation supports the debate on the difficulty
to define effective biomarkers for cancer (The ability of Cmap
methodology to virtually use an unlimited amount of genes to define
a signature seems, however, to be a powerful asset in carcinogenicity
Importantly, our reference set (and validating set) used only PHH,
a non-cancer cell model, and thus our positive connection using in vivo cancer signature, cannot be biased by deviations in normal
expression patterns resulting from the common criteria with the can-
cer phenotype, as the immortal state. Indeed, we tried to mix PHH
assays with HepG2 cells assays to build our reference data set and
observed that HepG2 assays were globally more connected to query-
defined HCC states than PHH assays, independently of the com-
pound carcinogenicity classification (data not shown). To avoid this
putative bias, and because our ultimate goal is to find an alternative in Fig. 3. SscMap plot results on the validation set. Each dot represents a vitro method to predict carcinogenicity at the earliest possible stage,
compound instance (three different concentrations per compound, always at
we decided to remove all liver cancer cell models and work only with
24 h exposure time). The color code indicate the compound concentration
(green for low dose, blue for middle dose and red for high dose), and form
Moreover, Cmap seems to be able to differentiate compound carci-
represent the compound (a dot for the original TG_Gates aflatoxin, a triangle
nogenicity based on their incubation concentration in vitro. Thus, our
for our validation aflatoxin and a diamond for Benzo[a]pyrene validation set).
signature geneset better classifies the known human hepatocarcino-
The green horizontal line present the threshold corrected for multiple testing.
gens (CHL) at low concentration in vitro than at high concentration.
More importantly, a higher number of false positives appear at high
medium concentration, even if still positive, do not pass the threshold
dose for NC compounds. These results underline the importance of
for multiple testing.
applying low exposures in in vitro toxicological assays for predicting
genotoxicity and carcinogenicity, in order to avoid possible cytotoxic
dose responses. Unfortunately, the low-concentration condition is
missing for several compounds in the TG_Gates database. It would be
Here, we studied the reliability of the Cmap method for classify-
interesting to add more compounds, both known human liver carcino-
ing and predicting a compound's hepatocarcinogenicity. To reduce
gens and non-carcinogens, to our reference set to see how this query
all possible variables, the study was performed on a single cancer
would behave.
type (HCC) and used a unique liver cell model (PHH) exposed to
Since our best setting to predict carcinogenicity is at low compound
a wide range of different compounds, of which some were known
concentration and after 24 h of exposure, evaluating connectivity to
human liver carcinogens (CHL) and some known non-liver carcino-
the HCC signature of compounds with unknown carcinogenic proper-
gens. Using the sscMap software, our Cmap analysis establishes sig-
ties (UC group) at this setting may be quite informative. Of the 36
nificantly positive connections with all hepatocarcinogens available in
compounds in the TG_Gates data set within the UC group and tested
the TG_Gates data set at low concentration and at 24 h of exposure,
at low concentration, 10 are significantly positively connected with
and a negative connection with all but one of the NC compounds at
our query: omeprazole, diclofenac, glibenclamide, carbamazepine,
this same concentration.
triazolam, N-nitrosomorpholine, naphthyl isothiocyanate, proprano-
Most of the Cmap studies used the Cmap data set available at the
lol, methyltestosterone, amphotericin B, buthionine sulfoximine and
) as a reference set.
diethyl maleate (listed here in order of decreasing connectivity score).
Cmap reference data set contains more than 7000 expression profiles
However, as those compounds are mainly drugs used for diverse
representing 1309 compounds used on five different cultured human
applications, some of them could have been taken by the patient
cancer cell lines (MCF7, ssMCF7, HL60, PC3 and SKMEL5). We
associated with the liver cancer samples. For instance, omeprazole,
decided not to use this reference data set as none of the five cell
a proton pump inhibitor, is one of the most widely prescribed drugs
lines are related to liver and thus the Cmap reference data set is not
internationally. Diclofenac is often used to treat chronic pain associ-
optimized for liver carcinogenicity prediction.
ated with cancer, and thus may have induced a change of expression
Interestingly, our signature geneset, based on an in vivo liver cancer
in the patient's liver, correlated with the gene expression induced by
expression profile, contains many more genes than usual biomarker
this compound in the in vitro TG_Gates and then inducing a connec-
lists representing a biological signature. However, with a geneset of
tion between the two profiles. The complete donor drug prescription
8934 genes (7520 upregulated and 1414 downregulated) selected
would be necessary to sort out the real carcinogenic compound from
without any expression threshold, the advantage is that we were able
the false positive.
to keep all small genes expression variations observed ‘in liver' can-
Another possible way to increase prediction accuracy would be to
cer samples in vivo. Internal testing with many different lists of genes
use other types of ‘omics' information in the reference dataset such
Compound carcinogenicity using connectivity mapping
as proteomics, thus increasing the amount of possible connections.
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